1. Field of the Invention
The invention is in the field of image analysis and more specifically in the field of color identification in digital images.
2. Related Art
The identification of an object's color can be useful, for example when attempting to locate and/or identify the object. However, when an object is viewed through a camera a robust perceptual color of the object may not be readily apparent. For example, an orange object can appear to be partly purple, pink, or brown under different lighting conditions and when observed using different cameras. The robust perceptual color, or the true color, of an object is informally defined to be the object's color perceived under some standard viewing, lighting, and sensing configurations (e.g., when the surface is viewed head on under neutral lighting conditions.) Several factors may lead to an observed color that is different from a robust perceptual color. These factors include the physical content of the scene surrounding the object, the angle at which the object is viewed, object surface characteristics, the illumination of the object, characteristics of the camera, etc. Thus, the observed color of an object can change as the object moves between regions of different illumination and as it is observed at different angles or when using different cameras.
Despite decades of research in color identification, algorithms capable of reliably identifying the robust perceptual color of an object in motion have not yet been developed. Most of the existing algorithms assume constant or slowly changing scene illumination. Further, most color identification algorithms depend on reliable estimates of parameters such as angles between light sources and the object, reflection angles, and surface materials. Typical approaches involve complicated nonlinear mathematical relationships and analysis that would be extremely difficult in real-time. Further, the required parameters can be unknown or difficult to determine in practice. Thus, the requirements of existing color identification algorithms make them impractical for use in most real world situations, particularly when an object is moving.
Surveillance applications are an example of real world situations where existing algorithms have failed to achieve robust perceptual color identification. In surveillance applications, one or more camera is used to observe an area and the movement of objects, such as people, within that area. For example, a camera may be used to observe a parking lot, secured area, or airport lobby. Existing algorithms are incapable of identifying the robust perceptual color of clothing worn by a person moving through one of these observed areas in real-time. Therefore, robust perceptual color is unavailable as a tool for identifying the moving person. There is, therefore, a need for improved systems and methods of identifying the robust perceptual color of an object.